惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园_首页
H
Help Net Security
量子位
The Cloudflare Blog
博客园 - Franky
博客园 - 聂微东
博客园 - 司徒正美
Last Week in AI
Last Week in AI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Apple Machine Learning Research
Apple Machine Learning Research
宝玉的分享
宝玉的分享
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
有赞技术团队
有赞技术团队
罗磊的独立博客
GbyAI
GbyAI
雷峰网
雷峰网
T
The Blog of Author Tim Ferriss
Martin Fowler
Martin Fowler
S
SegmentFault 最新的问题
美团技术团队
阮一峰的网络日志
阮一峰的网络日志
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
U
Unit 42
MongoDB | Blog
MongoDB | Blog

Hacker News: Show HN

PurrrrrFocus: Pomodoro Timer App - App Store Workflow Engine — Multi-Step Orchestration for Bun RapidPhoto: Pro Photo Editor App - App Store GitHub - DheerG/swarms: Achieve extraordinary results with claude code across a variety of tasks SPICE simulation → oscilloscope → verification with Claude Code — Lucas Gerads Show HN: VCoding – A 5 MB native Windows IDE with no dynamic dependencies Show HN: LLMs don't hallucinate because they're bad at math, it's the format GitHub - Agent-FM/agentfm-core: AgentFM is a peer-to-peer network that turns everyday computers into a decentralized AI supercomputer. AgentFM lets you run massive AI workloads directly across a global mesh of idle CPUs and GPUs. Show HN: Tracking Top US Science Olympiad Alumni over Last 25 Years GitHub - Potarix/agent-hub: One place to talk to all your agents Show HN: Runtime security for AI agents(injection,tool abuse, data exfiltration) GitHub - dubeyKartikay/lazyspotify: Terminal Spotify client for macOS and Linux GitHub - the-banana-tool/king-louie: Easy to use GUI Personal AI Assistant. Win/Linux/Mac. Show HN I made my vacation rental bookable by AI agents–no Airbnb, 0% commission GitHub - basteez/jsf-autoreload: maven plugin to enable hot reload on jsf projects uvm32/hosts/host-gdbstub at main · ringtailsoftware/uvm32 GitHub - labsai/EDDI: Config-driven engine that turns JSON into production-grade AI agents. Multi-agent orchestration, 12+ LLM providers, MCP/A2A protocols, RAG, persistent memory, and enterprise compliance (EU AI Act, GDPR, HIPAA). Built on Quarkus. GitHub - glitchnsec/fortyone-oss: AI Executive Assistant Platform Quickstart | Alien GitHub - muxshed/shed: One stream in, or many. Every destination, simultaneously. No cloud middleman, no per-channel fees, no limits. GitHub - ocrbase-hq/ocrbase: 📄 PDF/IMG ->.MD/JSON Document OCR API for PaddleOCR and GLMOCR. Self-hostable. GitHub - impactjo/home-memory: MCP server that lets your AI assistant remember everything about your home. GitHub - Sets88/dbcls: DbCls is a powerful terminal database client that supports various databases GitHub - neptun2000/heor-agent-mcp GitHub - SeanFDZ/macmind: Single-layer transformer in HyperTalk for the classic Macintosh RollQuation: Math Puzzles - Apps on Google Play GitHub - dropbox/witchcraft Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis GitHub - opentalon/opentalon: OpenTalon is an open-source platform built from the ground up in Go as a robust alternative to OpenClaw LinkedIn™ 职位抓取工具 - Chrome 应用商店
GitHub - davet47/heddle
davet47 · 2026-06-24 · via Hacker News: Show HN

CI

heddle regenerating a unit: one ~300-token context packet plus a cached verification, instead of re-reading whole files

Heddle treats software units as content-addressed contracts rather than files. An MCP server that makes agent regeneration loops cheap.

Because contracts are content-addressed and dependency-aware, agents reuse verification, compute blast radius precisely, and regenerate code from a few hundred tokens of context instead of re-reading whole files. Build systems ask which files changed. Heddle asks which software obligations changed.

The heddle is the part of a loom that holds the warp threads, the fixed, durable strands, while the shuttle weaves disposable weft through them. Contracts are warp. Code is weft.

The problem

Agents repeatedly pay to rediscover software structure. Spec-driven development tools made specs the durable artifact and code regenerable, but they run on plain files, so every regeneration loop re-derives what the project already knows:

  1. Context acquisition is expensive. Regenerating one unit means re-reading whole spec and source files: thousands of tokens to learn what a few hundred convey.
  2. Verification is uncached. Every regeneration re-runs (and re-reads the output of) the full relevant test surface, even for units whose contracts haven't changed.
  3. Blast radius is by convention, not mechanism. When a spec changes, nothing tells the agent precisely which dependents are invalidated.

The model

Heddle treats each software unit as a content-addressed contract with explicit dependencies, not a file. A contract is a small YAML spec (signature, invariants, examples, dependency names); the implementation behind it is regenerable weft. Because every contract is hashed and its dependencies are named, the structure an agent keeps re-deriving from files becomes something heddle computes once and serves.

Outcomes

The model buys three things, all mechanical:

  • Verification caching. A green test result is keyed on the contract, implementation, and dependency hashes, and served from cache until one of them changes. pytest runs only on a real miss.
  • Mechanical blast radius. A contract change reports the exact set of invalidated dependents, transitively and by hash, not by convention.
  • Tiny context packets. An agent regenerating a unit gets the contract, its dependencies' signatures, and its callers as one packet of a few hundred tokens, instead of the whole file closure.

The number

Same three regeneration tasks on a 20-contract sample project, once with raw file reads, once through heddle (tiktoken cl100k, reproduce with uv run python bench/benchmark.py):

task raw files heddle reduction
revenue_by_region 1,925 371 5.2x
top_customers 2,137 334 6.4x
revenue_by_category 1,942 392 5.0x
total 6,004 1,097 5.5x

Raw mode counts what a file-based agent reads per task: the unit's spec file, every transitive dep's spec file, every source module in the dep closure, the unit's test file, and the output of running the suite. It is deliberately generous to the baseline: it assumes the agent already knows the exact dependency closure, which is precisely the thing heddle computes for you.

Quickstart

pip install heddle-mcp
# or from source: pip install "git+https://github.com/davet47/heddle"

cd your-project
heddle init                 # creates .heddle/ and contracts/
heddle index                # builds the store from contracts/

Point Claude Code at it:

claude mcp add heddle -- heddle serve

(Stdio transport; the server resolves the project by walking up from its working directory to the nearest .heddle/.)

Contracts

One YAML file per unit in contracts/. Minimal, hand-writable, hashable:

name: revenue_by_region
signature: "(sales: list[Sale]) -> dict[Region, float]"
deps: [Sale, Region]            # other contract names
invariants:
  - excludes sales where completed is false
  - excludes sales with null amount
examples:
  - in:  "[Sale(region='QLD', amount=10, completed=True)]"
    out: "{'QLD': 10.0}"
tests: [tests/test_revenue.py::test_revenue_by_region]   # pytest node IDs
impl: src/revenue.py::revenue_by_region                  # current woven weft

Subdirectories are namespaces: contracts/billing/invoice.yaml is the contract billing/invoice, so the same short name can live in different folders. A contract's name must match its path under contracts/.

When to write a contract

A contract belongs on a stable seam: an interface other units depend on and that you expect to outlive its current implementation. The implementation behind it is disposable weft, regenerated freely. Dropping a contract where it does not earn that place is correct use, not a failure. The failure mode is the opposite, over-pinning interiors you would happily rewrite, which turns the durable layer into busywork.

Contracts are reviewed artifacts. Authoring one is cheap and getting cheaper, so the real cost is reviewing it, not writing it. A wrong contract is worse than no contract, because the durable artifact now lies: agents will regenerate code to satisfy a spec that is itself incorrect. Review a contract the way you review an interface, not the way you skim generated code.

Hashing semantics

  • Contract hash: sha256 over a canonical form: keys sorted, whitespace normalised, comments stripped, invariant and example order preserved, dep order ignored. impl and tests are excluded, so relocating files never invalidates. Invariants are free text and live inside this hash, so rewording one without changing its meaning still moves the contract hash and re-verifies every dependent. Behaviour-equivalent prose edits are not free yet (see Roadmap).
  • Impl hash: sha256 over the normalised AST of the implementation, so reformatting and comment edits never bust the cache. Docstrings are stripped too.
  • Verification key: (contract hash, impl hash, transitive dep contract hashes). Heddle caches verification results, not correctness: a cached green result is served iff the full key matches, and an edit to any contract in the closure forces a re-run. Failures are never served from cache. Two caveats are worth knowing. A cached pass assumes deterministic tests, so a green result that depended on wall-clock time, network, or randomness can outlive the condition that made it pass. And test source is not yet part of the key, so editing a test body without touching the contract or impl does not by itself force a re-run (see Roadmap).

MCP tools (the entire surface)

tool does
get_contract the ~300-token context packet: contract + hash + one-line dep signatures + caller list
put_contract validate, write contracts/<name>.yaml, return new hash + every invalidated dependent
get_dependents blast-radius query, direct or transitive, names + hashes
verify per-unit cached-pass / pass / fail; runs pytest only on cache misses; failures come back as a ≤40-token assertion summary, never a traceback
status dirty contracts, stale verifications, cache hit-rate, resolved verify interpreter, cumulative token counters

Every tool returns structured errors — {"error": {"code": "unknown_dep", "message": "'Regoin' not found — nearest: 'Region'"}} — never a stack trace.

The verify interpreter

verify runs your tests with the project's own python, resolved in order: heddle serve --python PATH.heddle/config.json ({"python": "..."}) → an auto-detected <project>/.venv → the interpreter running heddle. So a globally-installed heddle can verify a project against its own virtualenv without being installed into it; heddle status shows which interpreter it resolved.

.heddle/config.json also takes verify_timeout (seconds per pytest run, default 300) for suites that need longer than the default, and pycache_trust (default true); set pycache_trust: false — or pass --no-pycache-trust — to clear the project's __pycache__ before each verify run, so a stale .pyc can never shadow the current source.

CLI

heddle init · heddle index · heddle serve · heddle status · heddle verify. The sqlite store under .heddle/ is derived state: delete it any time and heddle index rebuilds it from contracts/.

heddle verify <name>… runs the same cached verification as the MCP tool from the command line and exits nonzero if any unit fails — drop it in CI or a pre-commit hook.

Try the sample project

cd examples/sales
heddle init && heddle index && heddle serve   # then point your agent at it

20 contracts, 25 tests, three dependency layers deep.

Development

uv sync
uv run pytest             # full suite; hash stability is the load-bearing suite
uv run python bench/benchmark.py

Python-only and single-process by design for v0.1. Everything not in this README is an issue.

License

Apache 2.0